diff --git a/ml/serving/policy_server.py b/ml/serving/policy_server.py
index d7e2db7..1676ab6 100644
--- a/ml/serving/policy_server.py
+++ b/ml/serving/policy_server.py
@@ -52,7 +52,8 @@ from ml.config.scope import (
 )
 from ml.encoder.state_encoder import CardEmbeddingIndex, StateEncoder
 
-logger = logging.getLogger("ml.serving")
+# Unique logger name so log lines are attributable to this module specifically
+logger = logging.getLogger("ml.serving.policy_server")
 
 
 def _unwrap_logits(raw):
@@ -175,7 +176,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             self._load_error = str(e)
-            logger.error("Failed to load checkpoint: %s", e)
+            logger.error("Failed to load checkpoint: %s", e, exc_info=True)
             return False
 
     def reload(self, checkpoint_path: str = None) -> bool:
@@ -207,7 +208,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             self._load_error = str(e)
-            logger.error("Hot-reload failed: %s", e)
+            logger.error("Hot-reload failed: %s", e, exc_info=True)
             return False
 
     def predict(
@@ -286,7 +287,7 @@ class PolicyInferenceService:
 
         except Exception as e:
             elapsed_ms = (time.time() - t_start) * 1000
-            logger.error("Inference error: %s", e)
+            logger.error("Inference error: %s", e, exc_info=True)
             return {
                 "error": str(e),
                 "inference_ms": round(elapsed_ms, 2),
@@ -346,7 +347,7 @@ class PolicyInferenceService:
             )
 
         except Exception as e:
-            logger.error("Batch inference error: %s", e)
+            logger.error("Batch inference error: %s", e, exc_info=True)
             results = [{"error": str(e)}] * len(snapshots)
 
         return results
diff --git a/routes/policy.py b/routes/policy.py
index 95f1f84..32e2d65 100644
--- a/routes/policy.py
+++ b/routes/policy.py
@@ -1,5 +1,5 @@
 """Commander AI Lab — Policy Decision Routes (Phase 2)
-════════════════════════════════════════════════════
+════════════════════════════════════════════════════════
 Live Forge ↔ policy server IPC endpoints for online training.
 These endpoints are purpose-built for the real-time decision loop
 where Forge's Java side calls the Python policy server at each
@@ -24,7 +24,8 @@ import numpy as np
 from fastapi import APIRouter, HTTPException
 from pydantic import BaseModel, Field
 
-logger = logging.getLogger("routes.policy")
+# Unique logger name so log lines are attributable to this module specifically
+logger = logging.getLogger("routes.policy.decide")
 
 router = APIRouter(prefix="/api/policy", tags=["policy"])
 
@@ -310,7 +311,10 @@ async def decide(req: DecideRequest):
                 req.snapshot_schema, req.resolved_turn(),
             )
         except Exception as e:
-            logger.warning("Precomputed vector encode failed (%s) — falling back", e)
+            logger.warning(
+                "Precomputed vector encode failed — falling back to full encoder",
+                exc_info=True,
+            )
             state_vec = None
     else:
         state_vec = None
@@ -373,7 +377,7 @@ async def collect_tuples(req: CollectRequest):
         try:
             _online_store.add_tuples(req.tuples)
         except Exception as e:
-            logger.error("Failed to store tuples: %s", e)
+            logger.error("Failed to store tuples: %s", e, exc_info=True)
             raise HTTPException(500, detail=str(e))
 
     return {
@@ -402,7 +406,7 @@ async def submit_reward(req: RewardRequest):
                 winner_seat=req.winner_seat,
             )
         except Exception as e:
-            logger.error("Failed to assign reward: %s", e)
+            logger.error("Failed to assign reward: %s", e, exc_info=True)
             raise HTTPException(500, detail=str(e))
 
     return {
@@ -563,7 +567,7 @@ def _infer_from_vector(state_vec: np.ndarray, policy_service, temperature: float
         }
     except Exception as e:
         elapsed_ms = (time.time() - t_start) * 1000
-        logger.error("Direct inference error: %s", e)
+        logger.error("Direct inference error in _infer_from_vector: %s", e, exc_info=True)
         return {"error": str(e), "inference_ms": round(elapsed_ms, 2)}